PCT Patent Publication No. WO 2010/044683 describes a personalised modelling method and system as graphically shown in FIG. 1a. The input variables/features (V), their weighting (W), the number K and the neighbourhood of samples (S), as well the model Mx and its parameters (P) are optimised together as a common chromosome used in a genetic algorithm (GA) (FIG. 1b). This method is explored on different applications in [2,29].
A disadvantage of the prior art is its inability to work on spatio-temporal data for an early classification of outputs resulting from spatio-temporal patterns of data.
Spatio- and spectro-temporal data (SSTD) that are characterised by a strong temporal component are the most common types of data collected in many domain areas. However, there is lack of efficient methods for modelling such data. In particular there is a need for spatio-temporal pattern recognition that can facilitate better understanding, better interpretation and new discoveries from complex SSTD and produce more accurate results including early prediction of spatio-temporal events. This is especially relevant for serious personal events such as stroke and heart failure, where it would be desirable to predict these events accurately and at the earliest time possible. This is also crucial for personalised brain data processing and personalised brain-computer interfaces (BCI). But this also relates to ecological and environmental event prediction, such as earthquakes and spread of invasive species, to biological events, such as gene and protein expression, etc.
It is an object of the present invention to provide an improved method and system to address this challenge.
Alternatively, it is an object of the present invention to address the foregoing problems or at least to provide the public with a useful choice.